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Category: Predictive Modelling

Building an F1 prediction engine – Predictive Modelling Part II

Building an F1 prediction engine – Predictive Modelling Part II

This post will describe and explain maybe the most critical part of predictive modelling: how to correctly estimate the performance of a machine learning model. This is performed by setting up a trusted cross-validation framework. It’s crucial to get this right, otherwise your model performance estimates will not reflect the true model performance.

Formula One 2 Vec

Formula One 2 Vec

You know what is the most geeky way to ‘prove’ that Alonso is the best F1 driver ever? It is Neural Embeddings! In this post I will try to give an intuitive explanation of what neural embeddings are, how they can be calculated and show some examples of how they capture semantic information about the…

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Building an F1 prediction engine – Predictive Modelling Part I

Building an F1 prediction engine – Predictive Modelling Part I

In the previous series of posts I discussed and explained the steps involved in Feature Engineering. In this series, I will talk about the coolest part of applied ML; the Predictive Modelling phase. This is where you get to use all the ‘magic’ power of machine learning algorithms and see the performance of any models…

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Model Interpretability with SHAP

Model Interpretability with SHAP

In applied machine learning, there is usually a trade-off between model accuracy and interpretability. Some models like linear regression or decision trees are considered interpretable whereas others, such as tree ensembles or neural networks, are used as black-box algorithms. While this is partly true, there have been great advances in the model interpretability front in…

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